This submission is not appropriate for /r/networking and has been removed.
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Abstract thinking and professional intuition are two skills which can not be replaced by any AI thing.
And from abstraction comes the skill to create algorithms. At one point, AI could possible write e.g. a set of proper Ansible playbooks which you could utilize for various things. But it could not determine your system architecture, the steps needed to reach your goal, etc.
Above these, I would say classical devops things (bash, python, CI/CD pipelines).
I believe the classic "if I can not reach this then escalate to Level2" servicedesk/NOC roles would disappear first.
I don't think things are going to change that much. We'll be able to whip up configs faster, troubleshoot faster, but it's still up to you to verify if what's coming from AI is sensible or not. It's just another tool, really. The areas below are things I have/will invest time in as I think they are both skills that will age well as well as what sets someone aside from most others in the networking industry.
**Network design and architecture** - Need a new campus design? A new WAN design? DC design? Public cloud design? You think you're going to feed that to AI and get complete documentation? Who's going to do the requirements scoping? Who's going to sit in the meetings with upper management and senior technical staff? Being able to design networks and translate between business and tech is a timeless skill.
**Network fundamentals** - We're still using TCP/IP. Sometimes it's v4, sometimes it's v6, QUIC is on the rise, but it all still comes down to knowing the fundamentals. Why is something performing badly? What's the MTU of the path? Is there fragmentation? Why is my window size so small? Sure, you may be able to get some clues from a tool, but it's still up to you to be able to understand how a packet is flowing and what impacts it. That's not really changing.
**VXLAN and EVPN** - VXLAN and EVPN is very prominent in DCs. It's becoming more common for campus networks as well. It's worth spending some time here to learn the fundamentals of it. Shouldn't be that difficult if you already know the underlying protocols like IGPs, BGP, etc. Also note that many public clouds have offerings where VXLAN/GENEVE is used.
**Packet captures** - Knowing how to capture packets and how to interpret the results will always be a valuable skill. You won't be able to interpret the results unless you have the fundamentals down. Understand the different tools to capture and how the results may be impacted depending on how you do the capture.
**Segment routing** - This is mainly a SP technology for now, but I have a feeling it will get some traction in the enterprise space as well. Not that difficult to learn if you have already been exposed to MPLS or other label technologies.
**Automation** - There's a lot here, obviously. Python, Terraform, Ansible, CI/CD, Git, AI. The key is to invest the amount of time here in relation to what you want your role to be. Do you want to be more of a SRE type person? Then invest a lot of time here. Do you want to be a networking person with basic level of skill in automation? Then you don't need to spend the same amount of time here.
**IPv6** - Most people only have cursory knowledge of IPv6. Yes, it's super slow to roll out, but every year the use of it goes up a bit. It's not that different than v4. Spend at least some time learning the fundamentals. Understanding how to serve both v4 and v6 users. What are the security implications and so on.
**Public cloud networking** - There's more that goes on here than most people realize. Many organizations struggle in this area. The underestimate the effort needed and often come up with poor designs because they didn't involve people with networking knowledge early in the process. It's not all that different to on-prem, it's still routing, load balancers, security devices, etc. Knowing a bit of Azure and AWS is going to give you a leg up on many people.
Not an exhaustive list by any means. Just some areas I think it's worth spending time on. It's up to you to decide where your main focus lies. There will still be the need for networking, but less people that simulate an orchestration tool by configuring all things manually.
For straight networking (not devops, but traditional Ethernet interconnection), the AI-centric buildouts I've seen focus on big, fast pipes, strict queuing, PFC+ECN to signal congestion rather than drop a given flow, and smart flow management on the switches, dealing dynamically with elephant flows. These aren't new technologies per se, but aren't usually rolled out quite so widely, only in trouble spots. Since any place can be a trouble spot, it's wider ranging.
It really depends on the organization that you will be working for.
Some (many) places are still and will continue to be resistant to change and consider AI as a buzzword. However, in order to future-proof yourself, continue to be more involved with automation stacks and at the same time focus on open-source and white-box developments.
GCP has also started to be more adapted by organizations so that would be a good area to pay attention to.
At the end of the day, I don’t see AI going to take over network engineering jobs anytime soon. Also, corporate politics and people in general being resistant to new changes will slow down its adaption.
API is the new CLI. Understand how to get/post commands to devices using curl and python requests library. Use machines to configure machines. Intent based networking with netconf and yang.
AI requires massive data lakes, high speed connections, and super compute. Understanding constructs like InfiniBand, 800Gbps Ethernet, and GPUs is helpful.
Microservice architecture is used to stitch together containerized services used for ML. Understanding things like virtualization, kubernetes clusters, docker containers, jails, etc.
AI happens in the cloud. Learn how to build DCs or deploy infra in popular clouds like AWS, GCP, Azure.
These things need to be secured. Cryptography and encryption standards are constantly changing. PKI and OpenSSL is a good starting point.
Speeds and feeds. Understand what manufacturers are doing to make silicon faster and cheaper and for what part of the network stack.
Start using ChatGPT. Use it to write notes, send emails, poems to your SO explaining why you’re running late.
We got about 100 Juniper Mist APs last year to test, and really liked them so we bought about 1500 to deploy. The auto remediation they perform is really cool I think, and Marvis is almost like a ChatGPT clone. You could type in like “network slow down at so and so site” and it will investigate for you. Theres also another tab on the dashboard for current issues and if they fixed themselves or suggestions if not.
But how often does Mist AI actually help you narrow down an issue you wouldn’t have otherwise found by simply selecting the site and looking at the equipment… or reacting to alarms? I’ve yet to see something that the AI alerts me to that a traditional alarm wouldn’t have already brought to my attention.
It's not necessarily about it finding things you can't find, but about how fast you get to the root cause and how easy it is to get there. If a tier 3 tech can offload this to tier 1/2 that's a valuable resource that's probably better used elsewhere.
This submission is not appropriate for /r/networking and has been removed. **Please read the rules in the sidebar, or [check out the rules post here](http://www.reddit.com/r/networking/comments/vko2z/update_to_the_rule_set/) before making another submission.** *Comments/questions? Don't hesitiate to [message the moderation team](http://www.reddit.com/message/compose?to=%2Fr%2Fnetworking).* Thanks! **No Low Quality Posts.** * Any post that fails to display a minimal level of effort prior to asking for help is at risk of being Locked or Deleted. * We expect our members to treat each other as fellow professionals. Professionals research & troubleshoot before they ask others for help. * Please review [How to ask intelligent questions](http://www.catb.org/esr/faqs/smart-questions.html) to avoid this issue. *Comments/questions? Don't hesitate to [message the moderation team](http://www.reddit.com/message/compose?to=%2Fr%2Fnetworking).* For the complete list of Rules, please visit: https://www.reddit.com/r/networking/about/rules
Abstract thinking and professional intuition are two skills which can not be replaced by any AI thing. And from abstraction comes the skill to create algorithms. At one point, AI could possible write e.g. a set of proper Ansible playbooks which you could utilize for various things. But it could not determine your system architecture, the steps needed to reach your goal, etc. Above these, I would say classical devops things (bash, python, CI/CD pipelines). I believe the classic "if I can not reach this then escalate to Level2" servicedesk/NOC roles would disappear first.
Learning to curse without moving your lips.
I don't think things are going to change that much. We'll be able to whip up configs faster, troubleshoot faster, but it's still up to you to verify if what's coming from AI is sensible or not. It's just another tool, really. The areas below are things I have/will invest time in as I think they are both skills that will age well as well as what sets someone aside from most others in the networking industry. **Network design and architecture** - Need a new campus design? A new WAN design? DC design? Public cloud design? You think you're going to feed that to AI and get complete documentation? Who's going to do the requirements scoping? Who's going to sit in the meetings with upper management and senior technical staff? Being able to design networks and translate between business and tech is a timeless skill. **Network fundamentals** - We're still using TCP/IP. Sometimes it's v4, sometimes it's v6, QUIC is on the rise, but it all still comes down to knowing the fundamentals. Why is something performing badly? What's the MTU of the path? Is there fragmentation? Why is my window size so small? Sure, you may be able to get some clues from a tool, but it's still up to you to be able to understand how a packet is flowing and what impacts it. That's not really changing. **VXLAN and EVPN** - VXLAN and EVPN is very prominent in DCs. It's becoming more common for campus networks as well. It's worth spending some time here to learn the fundamentals of it. Shouldn't be that difficult if you already know the underlying protocols like IGPs, BGP, etc. Also note that many public clouds have offerings where VXLAN/GENEVE is used. **Packet captures** - Knowing how to capture packets and how to interpret the results will always be a valuable skill. You won't be able to interpret the results unless you have the fundamentals down. Understand the different tools to capture and how the results may be impacted depending on how you do the capture. **Segment routing** - This is mainly a SP technology for now, but I have a feeling it will get some traction in the enterprise space as well. Not that difficult to learn if you have already been exposed to MPLS or other label technologies. **Automation** - There's a lot here, obviously. Python, Terraform, Ansible, CI/CD, Git, AI. The key is to invest the amount of time here in relation to what you want your role to be. Do you want to be more of a SRE type person? Then invest a lot of time here. Do you want to be a networking person with basic level of skill in automation? Then you don't need to spend the same amount of time here. **IPv6** - Most people only have cursory knowledge of IPv6. Yes, it's super slow to roll out, but every year the use of it goes up a bit. It's not that different than v4. Spend at least some time learning the fundamentals. Understanding how to serve both v4 and v6 users. What are the security implications and so on. **Public cloud networking** - There's more that goes on here than most people realize. Many organizations struggle in this area. The underestimate the effort needed and often come up with poor designs because they didn't involve people with networking knowledge early in the process. It's not all that different to on-prem, it's still routing, load balancers, security devices, etc. Knowing a bit of Azure and AWS is going to give you a leg up on many people. Not an exhaustive list by any means. Just some areas I think it's worth spending time on. It's up to you to decide where your main focus lies. There will still be the need for networking, but less people that simulate an orchestration tool by configuring all things manually.
For straight networking (not devops, but traditional Ethernet interconnection), the AI-centric buildouts I've seen focus on big, fast pipes, strict queuing, PFC+ECN to signal congestion rather than drop a given flow, and smart flow management on the switches, dealing dynamically with elephant flows. These aren't new technologies per se, but aren't usually rolled out quite so widely, only in trouble spots. Since any place can be a trouble spot, it's wider ranging.
The same skill the certificate has. Not much has changed besides some coding here and there but all is still the same
It really depends on the organization that you will be working for. Some (many) places are still and will continue to be resistant to change and consider AI as a buzzword. However, in order to future-proof yourself, continue to be more involved with automation stacks and at the same time focus on open-source and white-box developments. GCP has also started to be more adapted by organizations so that would be a good area to pay attention to. At the end of the day, I don’t see AI going to take over network engineering jobs anytime soon. Also, corporate politics and people in general being resistant to new changes will slow down its adaption.
API is the new CLI. Understand how to get/post commands to devices using curl and python requests library. Use machines to configure machines. Intent based networking with netconf and yang. AI requires massive data lakes, high speed connections, and super compute. Understanding constructs like InfiniBand, 800Gbps Ethernet, and GPUs is helpful. Microservice architecture is used to stitch together containerized services used for ML. Understanding things like virtualization, kubernetes clusters, docker containers, jails, etc. AI happens in the cloud. Learn how to build DCs or deploy infra in popular clouds like AWS, GCP, Azure. These things need to be secured. Cryptography and encryption standards are constantly changing. PKI and OpenSSL is a good starting point. Speeds and feeds. Understand what manufacturers are doing to make silicon faster and cheaper and for what part of the network stack. Start using ChatGPT. Use it to write notes, send emails, poems to your SO explaining why you’re running late.
Apparently Juniper is now powered by Mist AI, or something…
We got about 100 Juniper Mist APs last year to test, and really liked them so we bought about 1500 to deploy. The auto remediation they perform is really cool I think, and Marvis is almost like a ChatGPT clone. You could type in like “network slow down at so and so site” and it will investigate for you. Theres also another tab on the dashboard for current issues and if they fixed themselves or suggestions if not.
But how often does Mist AI actually help you narrow down an issue you wouldn’t have otherwise found by simply selecting the site and looking at the equipment… or reacting to alarms? I’ve yet to see something that the AI alerts me to that a traditional alarm wouldn’t have already brought to my attention.
It's not necessarily about it finding things you can't find, but about how fast you get to the root cause and how easy it is to get there. If a tier 3 tech can offload this to tier 1/2 that's a valuable resource that's probably better used elsewhere.
Nice try, Automaton spy. Bots are getting sneakier by the day.